Method and device for estimating orientation field of fingerprint

ABSTRACT

A method for estimating an orientation field of a fingerprint is provided, comprising: obtaining an initial orientation field of a fingerprint, and putting the initial orientation field in a reference coordinate system; obtaining N initial orientation blocks corresponding to the initial orientation field, and obtaining N orientation block sets corresponding to N positions respectively from a fingerprint dictionary; obtaining a similarity between each of the N initial orientation blocks and each orientation block in the N orientation block sets corresponding to the N positions to obtain P similarities, and selecting a preset number of candidate orientation blocks for each position from the orientation block set according to the P similarities; obtaining a compatibility between two candidate orientation blocks corresponding to any two adjacent positions respectively to obtain a plurality of compatibilities; and obtaining a candidate orientation block for each position according to the P similarities and the plurality of compatibilities.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and benefits of Chinese PatentApplication Serial No. 201310703526.1, filed with the State IntellectualProperty Office of P. R. China on Dec. 19, 2013, the entire content ofwhich is incorporated herein by reference.

FIELD

Embodiments of the present disclosure generally relate to a fingerprintidentification technology field, and more particularly to a method and adevice for estimating an orientation field of a fingerprint.

BACKGROUND

The fingerprint identification technology has been widely used invarious fields, including crime investigation, physical and logicalaccess control, time and attendance. The key technology of fingerprintidentification is to estimate an orientation field of a fingerprint. Inpractical applications, the collected fingerprint images may have verycomplicated background textures (such as fingerprint images collectedfrom a crime scene using chemical agents or optical instrument in thecrime investigation), or the fingerprint images may have poor quality ofthe fingerprint ridge.

In the prior art, the initial orientation field of the fingerprint canbe estimated according to local characteristics of the fingerprintimage, and then can be optimized by the smoothness constraint of thefingerprint ridge. Such method has a good performance when the qualityof the fingerprint image is high and the background of the fingerprintimage is clean enough. However, such method does not work when thefingerprint image has a strong interference of the background texture orhas a poor image quality.

As it is unable to ensure the accuracy of the initial orientation field,it is difficult to enhance and match with the initial orientation fieldof the fingerprint, and totally misleading results will be generated. Inthis case, the characteristics of the fingerprint image should bemanually extracted, and the extracted characteristics should be manuallymatched with characteristics of fingerprint in the fingerprint base toobtain the orientation field of the fingerprint. This requires highlyintensive human work, which is very complicated and time-consuming, andlow in efficiency.

Moreover, as the conventional method for estimating the orientationfield of the fingerprint only considers statistical information offingerprint image blocks and the smoothness constraint of thefingerprint ridge, it has following disadvantages. Firstly, in the caseof complicated background, it is difficult to distinguish whether anestimated initial orientation field relates to the background or to thefingerprint image if only the statistical information of the fingerprintimage blocks is considered. Secondly, although the smoothness of anestimated orientation field can be ensured, the correctness of theestimated orientation field cannot be ensured if only the smoothnessconstraint of the fingerprint ridge is considered.

SUMMARY

Embodiments of the present disclosure seek to solve at least one of theproblems existing in the prior art to at least some extent.

According to embodiments of a first aspect of the present disclosure, amethod for estimating an orientation field of a fingerprint is provided.The method comprises steps of: receiving a fingerprint to be estimated,obtaining an initial orientation field of the fingerprint to beestimated, and putting the initial orientation field in a referencecoordinate system; obtaining N initial orientation blocks correspondingto the initial orientation field, in which the N initial orientationblocks correspond to N positions of the initial orientation field in thereference coordinate system respectively, and obtaining N orientationblock sets corresponding to the N positions respectively from afingerprint dictionary, in which the fingerprint dictionary comprises Morientation block sets corresponding to M positions in the referencecoordinate system, and each of the M orientation block sets comprises aplurality of orientation blocks corresponding to a plurality of trainingfingerprints in one position of the M positions respectively and inwhich N≦M; obtaining a similarity between each of the N initialorientation blocks and each orientation block in the N orientation blocksets corresponding to the N positions to obtain P similarities, andselecting a preset number of candidate orientation blocks for each ofthe N positions from the orientation block set according to the Psimilarities; obtaining a compatibility between two candidateorientation blocks corresponding to any two adjacent positions of the Npositions respectively to obtain a plurality of compatibilities; andobtaining a candidate orientation block for each position from thepreset number of candidate orientation blocks according to the Psimilarities and the plurality of compatibilities.

In one embodiment of the present disclosure, the fingerprint dictionaryis established by: obtaining P training orientation fields correspondingto P training fingerprints respectively, and calibrating P referencepoints corresponding to the P training orientation fields respectivelyand P reference directions corresponding to the P training orientationfields respectively; adjusting the P training orientation fields to thereference coordinate system according to the P reference points and theP reference directions respectively, and obtaining P referenceorientation fields corresponding to the P training fingerprintsrespectively; and obtaining the M orientation block sets according tothe P reference orientation fields to establish the fingerprintdictionary.

In one embodiment of the present disclosure, obtaining the M orientationblock sets according to the P reference orientation fields comprises:obtaining a first preset size orientation block corresponding to aposition (x_(i),y_(i)) in each of the P reference orientation fields;judging whether the first preset size orientation block is in a validregion; if yes, putting the first preset size orientation block into avalid set T(x_(i),y_(i)) corresponding to the position (x_(i),y_(i));and obtaining an orientation block set D(x_(i),y_(i)) corresponding tothe position (x_(i),y_(i)) by clustering orientation blocks in the validset T(x_(i),y_(i)).

In one embodiment of the present disclosure, clustering orientationblocks in the valid set (x_(i),y_(i)) comprises: a. putting anyorientation block of the valid set T(x_(i),y_(i)) into the orientationblock set D(x_(i),y_(i)); b. selecting another orientation block fromthe valid set T(x_(i),y_(i)), and obtaining a similarity between theanother orientation block and each orientation block of the orientationblock set D(x_(i),y_(i)) to obtain a first plurality of similarities; c.if each of the first plurality of similarities is less than a firstpreset value, putting the another orientation block into the orientationblock set D(x_(i),y_(i)); d. if at least one of the first plurality ofsimilarities is larger than or equal to the first preset value,abandoning the another orientation block; and e. repeating step b to duntil all the orientation blocks of the valid set T(x_(i),y_(i)) havebeen selected.

In one embodiment of the present disclosure, obtaining an initialorientation field of the fingerprint to be estimated comprises:obtaining a foreground image of the fingerprint to be estimated;dividing the foreground image into a plurality of non-overlapping andsecond preset size image blocks; applying two-dimensional short-timeFourier transform to each of the plurality of non-overlapping and secondpreset size image blocks, and obtaining a plurality of responsedirections corresponding to the plurality of non-overlapping and secondpreset size image blocks respectively; establishing a foregroundorientation field of the fingerprint to be estimated according to theplurality of response directions; and generating the initial orientationfield of the fingerprint to be estimated by adjusting the foregroundorientation field according to an adjusting algorithm for fingerprintattitude.

In one embodiment of the present disclosure, selecting preset number ofcandidate orientation blocks for each of the N positions from theorientation block set corresponding to the initial orientation blockaccording to the P similarities comprises: f. obtaining an orientationblock queue by sorting orientation blocks of the orientation block setD(x_(i),y_(i)) according to the similarities corresponding to theorientation blocks of the orientation block set D(x_(i),y_(i)),obtaining a maximum similarity of the similarities, and putting theorientation block corresponding to the maximum similarity into acandidate orientation block set; g. selecting an orientation blocksubsequent to the orientation block put into the candidate orientationblock set from the orientation block queue, and obtaining a similaritybetween the orientation block subsequent to the orientation block putinto the candidate orientation block set and each orientation block inthe candidate orientation block set to obtain a second plurality ofsimilarities; h. if each of the second plurality of similarities is lessthan a second preset value, putting the orientation block subsequent tothe orientation block put into the candidate orientation block set intothe candidate orientation block set; i. if at least one of the secondplurality of similarities is larger than or equal to the second presetvalue, abandoning the orientation block subsequent to the orientationblock put into the candidate orientation block set; and j. repeatingstep g to i until the number of the candidate orientation blocks in thecandidate orientation block set is a preset number.

In one embodiment of the present disclosure, obtaining a candidateorientation block for each position from the preset number of candidateorientation blocks according to the P similarities and the plurality ofcompatibilities comprises: establishing an objective optimizationfunction according to the P similarities and the plurality ofcompatibilities, and obtaining the candidate orientation block for eachposition by optimizing the objective optimization function, in which theobjective optimization function is:

${{\min \mspace{14mu} {E(r)}} = {\min \left( {{\sum\limits_{i \in V}\left( {1 - {S\left( {\Theta_{i},\Phi_{i,r_{i}}} \right)}} \right)} + {w_{c}{\sum\limits_{{({i,j})} \in N}\left( {1 - {C\left( {\Phi_{i,r_{i}},\Phi_{j,r_{j}}} \right)}} \right)}}} \right)}},$

in which, V is the initial orientation field, i is a position(x_(i),y_(i)) in the initial orientation field V, Θ_(i) is an initialorientation block corresponding to the position (x_(i),y_(i)), Θ_(i,r)_(i) is an r_(i) ^(th) candidate orientation block corresponding to theposition (x_(i),y_(i)), S(Θ_(i),Θ_(i,r) _(i) ) is the similarity betweenthe initial orientation block Θi and the candidate orientation blockΘ_(i,r) _(i) , N is a set of four-connected adjacent initial orientationblocks, Θ_(j,r) _(j) is an r_(j) ^(th) candidate orientation blockcorresponding to a position (x_(j),y_(j)) adjacent to the position(x_(i),y_(i)), C(Θ_(i,r) _(i) ,Θ_(j,r) _(j) ) is the compatibilitybetween the candidate orientation block Θ_(i,r) _(i) and the candidateorientation block Θ_(j,r) _(j) , w_(c) is a preset weight.

In one embodiment of the present disclosure, the objective optimizationfunction is optimized according to Graph Cut or confidence transmissionmethod.

With the method according to embodiments of the present disclosure, thecandidate orientation block for each position of the referencecoordinate system is obtained according to the similarities between eachof the N initial orientation blocks and each orientation block in the Norientation block sets, and the compatibilities between two candidateorientation blocks corresponding to any two adjacent positions of the Npositions, and thus a rational orientation field of the fingerprint isestimated by global optimization. In addition, with the method accordingto embodiments of the present disclosure, the orientation field isquantized, pre-knowledge regarding a fingerprint ridge is effectivelyapplied to the estimation of orientation field of the fingerprint, aninterference of a complicate background is greatly reduced, anefficiency of the fingerprint identification is increased, and anidentification precision for a poor quality fingerprint is significantlyimproved.

According to embodiments of a second aspect of the present disclosure, adevice for estimating an orientation field of a fingerprint is provided.The device comprises: a first obtaining module, configured to receive afingerprint to be estimated, to obtain an initial orientation field ofthe fingerprint to be estimated, and to put the initial orientationfield in a reference coordinate system; a second obtaining module,configured to obtain N initial orientation blocks corresponding to theinitial orientation field, in which the N initial orientation blockscorrespond to N positions of the initial orientation field in thereference coordinate system respectively, and to obtain N orientationblock sets corresponding to the N positions respectively from afingerprint dictionary, in which the fingerprint dictionary comprises Morientation block sets corresponding to M positions in the referencecoordinate system, and each of the M orientation block sets comprises aplurality of orientation blocks corresponding to a plurality of trainingfingerprints in one position of the M positions respectively and inwhich N≦M; a filtering module, configured to obtain a similarity betweeneach of the N initial orientation blocks and each orientation block inthe N orientation block sets corresponding to the N positions to obtainP similarities, and to select a preset number of candidate orientationblocks for each of the N positions from the orientation block setaccording to the P similarities; a third obtaining module, configured toobtain a compatibility between two candidate orientation blockscorresponding to any two adjacent positions of the N positionsrespectively to obtain a plurality of compatibilities; and a generatingmodule, configured to obtain a candidate orientation block for eachposition from the preset number of candidate orientation blocksaccording to the P similarities and the plurality of compatibilities.

In one embodiment of the present disclosure, the device furthercomprises a dictionary establishing module configured to establish thefingerprint dictionary. The dictionary establishing module comprises: afirst obtaining unit, configured to obtain P training orientation fieldscorresponding to P training fingerprints respectively, and calibrating Preference points corresponding to the P training orientation fieldsrespectively and P reference directions corresponding to the P trainingorientation fields respectively; an adjusting unit, configured to adjustthe P training orientation fields to the reference coordinate systemaccording to the P reference points and the P reference directionsrespectively, and to obtain P reference orientation fields correspondingto the P training fingerprints respectively; and a second obtainingunit, configured to obtaining the M orientation block sets according tothe P reference orientation fields to establish the fingerprintdictionary.

In one embodiment of the present disclosure, the second obtaining unitis configured to: obtain a first preset size orientation blockcorresponding to a position (x_(i),y_(i)) in each of the P referenceorientation fields; judge whether the first preset size orientationblock is in a valid region; if yes, put the first preset sizeorientation block into a valid set T(x_(i),y_(i)) corresponding to theposition (x_(i),y_(i)); and obtain an orientation block setD(x_(i),y_(i)) corresponding to the position (x_(i),y_(i)) by clusteringorientation blocks in the valid set T(x_(i),y_(i)).

In one embodiment of the present disclosure, clustering orientationblocks in the valid set T(x_(i),y_(i)) comprises: a. putting anyorientation block of the valid set T(x_(i),y_(i)) into the orientationblock set D(x_(i),y_(i)); b. selecting another orientation block fromthe valid set T(x_(i),y_(i)), and obtaining a similarity between theanother orientation block and each orientation block of the orientationblock set D(x_(i),y_(i)) to obtain a first plurality of similarities; c.if each of the first plurality of similarities is less than a firstpreset value, putting the another orientation block into the orientationblock set D(x_(i),y_(i)); d. if at least one of the first plurality ofsimilarities is larger than or equal to the first preset value,abandoning the another orientation block; and e. repeating step b to duntil all the orientation blocks of the valid set T(x_(i),y_(i)) havebeen selected.

In one embodiment of the present disclosure, the first obtaining modulecomprises: a third obtaining unit, configured to obtain a foregroundimage of the fingerprint to be estimated; a dividing unit, configured todivide the foreground image into a plurality of non-overlapping andsecond preset size image blocks; a four obtaining unit, configured toapply two-dimensional short-time Fourier transform to each of theplurality of non-overlapping and second preset size image blocks, and toobtain a plurality of response directions corresponding to the pluralityof non-overlapping and second preset size image blocks respectively; aorientation field establishing unit, configured to establish aforeground orientation field of the fingerprint to be estimatedaccording to the plurality of response directions; and a generatingunit, configured to generate the initial orientation field of thefingerprint to be estimated by adjusting the foreground orientationfield according to an adjusting algorithm for fingerprint attitude.

In one embodiment of the present disclosure, the filtering module isconfigured to: obtain an orientation block queue by sorting orientationblocks of the orientation block set D(x_(i),y_(i)) according to thesimilarities corresponding to the orientation blocks of the orientationblock set D(x_(i),y_(i)), obtain a maximum similarity of thesimilarities, and put the orientation block corresponding to the maximumsimilarity into a candidate orientation block set; select an orientationblock subsequent to the orientation block put into the candidateorientation block set from the orientation block queue and obtain asimilarity between the orientation block subsequent to the orientationblock put into the candidate orientation block set and each orientationblock in the candidate orientation block set to obtain a secondplurality of similarities; if each of the second plurality ofsimilarities is less than a second preset value, put the orientationblock subsequent to the orientation block put into the candidateorientation block set into the candidate orientation block set; if atleast one of the second plurality of similarities is larger than orequal to the second preset value, abandon the orientation blocksubsequent to the orientation block put into the candidate orientationblock set; and repeat step g to i until the number of the candidateorientation blocks in the candidate orientation block set is a presetnumber.

In one embodiment of the present disclosure, the generating module isconfigured to establish an objective optimization function according tothe P similarities and the plurality of compatibilities, and to obtainthe candidate orientation block for each position by optimizing theobjective optimization function, in which the objective optimizationfunction is:

${{\min \mspace{14mu} {E(r)}} = {\min \left( {{\sum\limits_{i \in V}\left( {1 - {S\left( {\Theta_{i},\Phi_{i,r_{i}}} \right)}} \right)} + {w_{c}{\sum\limits_{{({i,j})} \in N}\left( {1 - {C\left( {\Phi_{i,r_{i}},\Phi_{j,r_{j}}} \right)}} \right)}}} \right)}},$

in which, V is the initial orientation field, i is a position(x_(i),y_(i)) in the initial orientation field V, Θi is an initialorientation block corresponding to the position (x_(i),y_(i)), Θ_(i,r)_(i) is an r_(i) ^(th) candidate orientation block corresponding to theposition (x_(i),y_(i)), S(Θ_(i),Θ_(i,r) _(i) ) is the similarity betweenthe initial orientation block Θ_(i) and the candidate orientation blockΦΘ_(i,r) _(i) , N is a set of four-connected adjacent initialorientation blocks, Θ_(j,r) _(j) is an r_(j) ^(th) candidate orientationblock corresponding to a position (x_(j),y_(j)) adjacent to the position(x_(i),y_(i)), C(Θ_(i,r) _(i) ,Θ_(j,r) _(j) ) is the compatibilitybetween the candidate orientation block Θ_(i,r) _(i) and the candidateorientation block Θ_(j,r) _(j) , w_(c) is a preset weight.

In one embodiment of the present disclosure, the objective optimizationfunction is optimized according to Graph Cut or confidence transmissionmethod.

With the device according to embodiments of the present disclosure, thecandidate orientation block for each position of the referencecoordinate system is obtained according to the similarities between eachof the N initial orientation blocks and each orientation block in the Norientation block sets, and the compatibilities between two candidateorientation blocks corresponding to any two adjacent positions of the Npositions, and thus a rational orientation field of the fingerprint isestimated by global optimization. In addition, with the device accordingto embodiments of the present disclosure, the orientation field isquantized, pre-knowledge regarding a fingerprint ridge is effectivelyapplied to the estimation of orientation field of the fingerprint, aninterference of a complicate background is greatly reduced, anefficiency of the fingerprint identification is increased, and anidentification precision for a poor quality fingerprint is significantlyimproved.

Additional aspects and advantages of embodiments of present disclosurewill be given in part in the following descriptions, become apparent inpart from the following descriptions, or be learned from the practice ofthe embodiments of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects and advantages of embodiments of the presentdisclosure will become apparent and more readily appreciated from thefollowing descriptions made with reference to the accompanying drawings,in which:

FIG. 1 is a flow chart of a method for estimating an orientation fieldof a fingerprint according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method for obtaining an initial orientationfield of a fingerprint to be estimated according to an embodiment of thepresent disclosure;

FIG. 3 is a flow chart of a method for establishing a fingerprintdictionary according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a reference point and a referencedirection of a training fingerprint according to an embodiment of thepresent disclosure;

FIG. 5a is a schematic diagram of a training orientation field before anadjustment according to an embodiment of the present disclosure;

FIG. 5b is a schematic diagram of the training orientation field afterthe adjustment according to an embodiment of the present disclosure;

FIG. 6 is a flow chart of a method for obtaining M orientation blocksets according to P reference orientation fields according to anembodiment of the present disclosure;

FIG. 7 is a schematic diagram of valid sets T(−3,−3) and T(3,3)corresponding to position (−3,−3) and position (3, 3) according to anembodiment of the present disclosure according to an embodiment of thepresent disclosure;

FIG. 8 is a schematic diagram of a comparison between the candidateorientation blocks selected according to similarities and the candidateorientation blocks selected according to both the similarities and adiversification constraint according to an embodiment of the presentdisclosure;

FIG. 9a is a schematic diagram of two candidate orientation blocks withhigh compatibility according to an embodiment of the present disclosure;

FIG. 9b is a schematic diagram of two candidate orientation blocks withlow compatibility according to an embodiment of the present disclosure;

FIG. 10 is a block diagram of a device for estimating an orientationfield of a fingerprint according to an embodiment of the presentdisclosure; and

FIG. 11 is a block diagram of a device for estimating an orientationfield of a fingerprint according to an embodiment of the presentdisclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in detail in thefollowing descriptions, examples of which are shown in the accompanyingdrawings, in which the same or similar elements and elements having sameor similar functions are denoted by like reference numerals throughoutthe descriptions. The embodiments described herein with reference to theaccompanying drawings are explanatory and illustrative, which are usedto generally understand the present disclosure. The embodiments shallnot be construed to limit the present disclosure.

It is to be understood that phraseology and terminology used herein(such as, terms like “center”, “longitudinal”, “lateral”, “length”,“width”, “thickness”, “up”, “down”, “front”, “rear”, “left”, “right”,“top”, “bottom”, “inside”, “outside”, “vertical”, “horizontal”,“clockwise” and “counterclockwise”) are only used to simplifydescription of the present invention, and do not indicate or imply thatthe device or element referred to must have or operated in a particularorientation. They cannot be seen as limits to the present disclosure.

It is to be understood that, in the description of the presentdisclosure, terms of “first” and “second” are only used for descriptionand cannot be seen as indicating or implying relative importance. Unlessotherwise stipulated and restricted, it is to be explained that terms of“linkage” and “connection” shall be understood broadly, for example, itcould be mechanical connection or electrical connection; it could bedirect linkage, indirect linkage via intermediate medium. Those skilledin the art shall understand the concrete notations of the termsmentioned above according to specific circumstances. Furthermore, unlessotherwise explained, it is to be understood that a term of “a pluralityof” refers to two or more.

Any procedure or method described in the flow charts or described in anyother way herein may be understood to comprise one or more modules,portions or parts for storing executable codes that realize particularlogic functions or procedures. Moreover, advantageous embodiments of thepresent disclosure comprises other implementations in which the order ofexecution is different from that which is depicted or discussed,including executing functions in a substantially simultaneous manner orin an opposite order according to the related functions. These and otheraspects should be understood by those skilled in the art with referenceto the following description and drawings. In these description anddrawings, some particular implementations of the present disclosure aredisclosed to present some ways for implementing the principle of thepresent disclosure. However, it should be understood that embodiments ofthe present disclosure is not limited to this. Contrarily, embodimentsof the present disclosure include all the variations, modifications andequivalents within the spirit and scope of the appended claims.

The present disclosure mainly solves problems of establishing afingerprint dictionary based on pre-knowledge of an orientation field ofa fingerprint and estimating the orientation field of the fingerprintwith poor quality and/or complicated background texture. For thefingerprint with poor quality and complicated background texture,misleading results may be generated according to a conventional methodwhich cannot ensure an accuracy of an initial orientation field. Asfingerprint experts who have pre-knowledge on a fingerprint range caneliminate an interference of strong noise, the characteristics of thefingerprint image should be manually extracted by the fingerprintexperts and then be matched with the fingerprints of the fingerprintbank. The pre-knowledge related to the fingerprint is not taken intoaccount, which causes disadvantages to conventional automaticalfingerprint identification system both in theory and in practice.Therefore, a model including the pre-knowledge on the fingerprint rangeneeds to be established. Then the orientation field of the fingerprintwith poor quality and complicated background texture can be estimatedaccording to the model, which reduces human work, increases anautomation, and improves an identification precision for the fingerprintwith the poor quality and complicated background texture.

It should be noted that, an orientation field generally refers to apixel orientation field and an image block orientation field. Inembodiments of the present disclosure, the orientation field refers tothe image block orientation fields. In other words, a fingerprint imageis divided into a plurality of image blocks having a preset size (suchas 16×16 pixels) and non-overlapped with each other, and an orientationof each of the plurality of image blocks can be an orientation of aridge and a valley in each image block. Then, the orientation field ofthe fingerprint image is formed by the orientations of the plurality ofimage blocks.

In the following, a method for estimating an orientation field of afingerprint according to embodiments of the present disclosure will bedescribed in detail with reference to drawings.

FIG. 1 is a flow chart of a method for estimating an orientation fieldof a fingerprint according to an embodiment of the present disclosure.As shown in FIG. 1, the method comprises the following steps.

At step 101, a fingerprint to be estimated is received, and an initialorientation field of the fingerprint to be estimated is obtained, andthe initial orientation field is put in a reference coordinate system.

FIG. 2 is a flow chart of a method for obtaining the initial orientationfield of the fingerprint to be estimated according to an embodiment ofthe present disclosure. As shown in FIG. 2, obtaining the initialorientation field of the fingerprint to be estimated comprises thefollowing steps.

At step 201, a foreground image of the fingerprint to be estimated isobtained.

In one embodiment of the present disclosure, if the fingerprint iscollected from a scene (such as a crime scene), the foreground imageshould be manually extracted. If the fingerprint is from a fingerprintbank, since a background thereof is very simple, the foreground imagecan be obtained by applying Fourier transform to the image blocks of thefingerprint respectively without manual extraction. Specifically, foreach image block, two waves having the most and the secondary strongestfrequency response are obtained by Fourier transform, and then theforeground image can be obtained by comparing a ratio of two amplitudesof the two waves with a threshold.

At step 202, the foreground image is divided into a plurality ofnon-overlapping and second preset size image blocks.

In one embodiment of the present disclosure, the second preset size canbe 16×16 pixels.

At step 203, two-dimensional short-time Fourier transform is applied toeach of the plurality of non-overlapping and second preset size imageblocks, and a plurality of response directions corresponding to theplurality of non-overlapping and second preset size image blocks areobtained respectively.

In one embodiment of the present disclosure, each image block can beregarded as a two-dimensional surface wave and can be processed by thetwo-dimensional short-time Fourier transform. Then, a strongest responseis found in a frequency domain, such that an orientation having thestrongest response is obtained by calculating an angle of the strongestresponse with respect to a center of the frequency domain.

At step 204, a foreground orientation field of the fingerprint to beestimated is established according to the plurality of responsedirections.

At step 205, the initial orientation field of the fingerprint to beestimated is generated by adjusting the foreground orientation fieldaccording to an adjusting algorithm for fingerprint attitude.

In one embodiment of the present disclosure, the foreground orientationfield can be adjusted to the initial orientation field in the referencecoordinate system according to any of existing adjusting algorithms forfingerprint attitude.

At step 102, N initial orientation blocks corresponding to the initialorientation field are obtained, in which the N initial orientationblocks correspond to N positions of the initial orientation field in thereference coordinate system respectively, and N orientation block setscorresponding to the N positions respectively are obtained from afingerprint dictionary, in which the fingerprint dictionary comprises Morientation block sets corresponding to M positions in the referencecoordinate system, and each of the M orientation block sets comprises aplurality of orientation blocks corresponding to a plurality of trainingfingerprints in one position of the M positions respectively and inwhich N≦M.

In one embodiment of the present disclosure, in order to ensure thefingerprint dictionary is real and representative, the fingerprintdictionary may be established according to statistical results oforientation fields of a group of real training fingerprints calibratedmanually.

FIG. 3 is a flow chart of a method for establishing the fingerprintdictionary according to an embodiment of the present disclosure. Asshown in FIG. 3 establishing the fingerprint dictionary comprises thefollowing steps.

At step 301, P training orientation fields corresponding to P trainingfingerprints respectively are obtained, and P reference pointscorresponding to the P training orientation fields respectively and Preference directions corresponding to the P training orientation fieldsrespectively are calibrated.

In one embodiment of the present disclosure, firstly, P valid areas withhigh image quality (i.e., high enough to tell orientation) arecalibrated manually in the P training fingerprints respectively.Subsequently, the P training orientation fields of the P trainingfingerprints are calibrated manually in the P valid areas respectively.Then the P reference points corresponding to the P training orientationfields respectively and the P reference directions corresponding to theP training orientation fields are calibrated manually.

FIG. 4 is a schematic diagram of a reference point and a referencedirection of a training fingerprint according to an embodiment of thepresent disclosure. Specifically, as shown in FIG. 4, the referencepoint (point r shown in FIG. 4) is a midpoint between point a and pointb shown in FIG. 4, and the reference direction may be directed frompoint b to point a, in which point a is a vertex of a lowest ridgeextending from left to right of the training fingerprint and in an upperpart of the training fingerprint, and point b is a midpoint of a highestridge in a lower part of the training fingerprint. Alternatively, thereference direction may be directed from point b to point a.

At step 302, the P training orientation fields are adjusted to thereference coordinate system according to the P reference points and theP reference directions respectively, and P reference orientation fieldscorresponding to the P training fingerprints respectively are obtained.

In one embodiment of the present disclosure, the P training orientationfields can be adjusted according to the reference coordinate system. Forinstance, the P training orientation fields are processed by a rotation,a translation and a direction interpolation respectively, such that thereference point of each training orientation field is adjusted to anorigin of the reference coordinate system, and the reference directionof each training orientation field is adjusted to a positive directionof y axis of the reference coordinate system. The adjusted trainingorientation field is used as the reference orientation field of thetraining fingerprint. FIG. 5a is a schematic diagram of the trainingorientation field before adjustment according to an embodiment of thepresent disclosure, and FIG. 5b is a schematic diagram of the trainingorientation field after adjustment according to an embodiment of thepresent disclosure.

At step 303, the M orientation block sets are obtained according to theP reference orientation fields to establish the fingerprint dictionary.

FIG. 6 is a flow chart of a method for obtaining the M orientation blocksets according to the P reference orientation fields according to anembodiment of the present disclosure. As shown in FIG. 6, obtaining theM orientation block sets according to the P reference orientation fieldscomprises the following steps.

At step 601, a first preset size orientation block corresponding to aposition (x_(i),y_(i)) in each of the P reference orientation fields isobtained.

In one embodiment of the present disclosure, the first preset sizeorientation block is obtained by sliding a first preset size window oneach of the P reference orientation fields from left to right and fromtop to bottom. In one embodiment of the present disclosure, the firstpreset size is d×d(in which d may be of any value, such as d=4), whichmeans that the window contains d×d image blocks (in one embodiment, asize of each image block may be 16×16 pixels). Each image block containsone direction of the reference orientation field. As the window slidesat the position (x_(i),y_(i)), one first preset size orientation blockis obtained.

At step 602, it is judged whether the first preset size orientationblock is in a valid region, and if yes, execute step 603.

Specifically, if each image block in the first preset size orientationblock contains a direction of the reference orientation field, the firstpreset size orientation block is in the valid region, and if a imageblock in the first preset size orientation block does not contain thedirection of the reference orientation field, the first preset sizeorientation block is not in the valid region.

At step 603, the first preset size orientation block is put into a validset T(x_(i),y_(i)) corresponding to the position (x_(i),y_(i)).

For each reference orientation field, if the first preset sizeorientation block corresponding to the position (x_(i),y_(i)) is in thevalid region of the reference orientation field, the first preset sizeorientation block will be put into the valid set T(x_(i),y_(i))corresponding to the position (x_(i),y_(i)).

Thus, one valid set containing a plurality of first preset sizeorientation blocks is obtained corresponding to the position(x_(i),y_(i)), and a plurality of valid sets are obtained correspondingto a plurality of positions in the reference coordinate system. FIG. 7is a schematic diagram of valid sets T(−3,−3) and T(3,3) correspondingto position (−3,−3) and position (3, 3) according to an embodiment ofthe present disclosure.

At step 604, an orientation block set D(x_(i),y_(i)) corresponding tothe position (x_(i),y_(i)) is obtained by clustering orientation blocksin the valid set T(x_(i),y_(i)).

As the number of the training fingerprints rising, the number oforientation blocks in each valid set rises, which will increase aworkload of fingerprint matching and reduce an efficiency of estimatingthe orientation field of the fingerprint. So each valid set should beclustered.

Firstly, an empty orientation block set D(x_(i),y_(i)) is initiallygiven, and any orientation block of the valid set T(x_(i),y_(i)) is putinto the orientation block set D(x_(i),y_(i)). Subsequently, anotherorientation block is selected from the valid set T(x_(i),y_(i)), and afirst plurality of similarities are obtained by obtaining a similaritybetween the another orientation block and each orientation block of theorientation block set D(x_(i),y_(i)). If each of the first plurality ofsimilarities is less than a first preset value, the another orientationblock will be put into the orientation block set D(x_(i),y_(i)). If atleast one of the first plurality of similarities is larger than or equalto the first preset value, the another orientation block will beabandoned. Clustering orientation blocks in the valid set T(x_(i),y_(i))is finished until all the orientation blocks in the valid setT(x_(i),y_(i)) are selected to be put into the orientation block setD(x_(i),y_(i)) or to be abandoned. For instance, as shown in FIG. 7,orientation block sets D(−3,−3) and D(3,3) corresponding to position(−3,−3) and position (3, 3) respectively are obtained by clustering thevalid sets T(−3,−3) and T(3, 3) respectively.

At step 103, P similarities are obtained by obtaining a similaritybetween each of the N initial orientation blocks and each orientationblock in the N orientation block sets corresponding to the N positions,and a preset number of candidate orientation blocks for each of the Npositions are selected from the orientation block set according to the Psimilarities.

Specifically, Q_(i) similarities are obtained as a similarity betweenthe initial orientation block corresponding to the position(x_(i),y_(i)) and each orientation block in the orientation block setD(x_(i),y_(i)) is obtained. Then, a preset number of candidateorientation blocks for the position (x_(i),y_(i)) are selected from theorientation block set according to the Q_(i) similarities. In oneembodiment of the present disclosure, the preset number may be 6.

It should be noted that the number Q_(i) may be different from Q_(i) inwhich Q_(i) is a number of similarities between the initial orientationblock corresponding to the position (x_(j),y_(j)) and each orientationblock in the orientation block set D(x_(j),y_(j)), and iε(1,N), jε(1,N).

In one embodiment of the present disclosure, selecting preset number ofcandidate orientation blocks for each of the N positions from theorientation block set corresponding to the initial orientation blockaccording to the P similarities comprises the following steps.

At step 1031, an orientation block queue is obtained by sortingorientation blocks of the orientation block set D(x_(i),y_(i)) accordingto the similarities corresponding to the orientation blocks of theorientation block set D(x_(i),y_(i)), a maximum similarity of thesimilarities is obtained, and the orientation block corresponding to themaximum similarity is put into a candidate orientation block set.

At step 1032, an orientation block subsequent to the orientation blockput into the candidate orientation block set is selected from theorientation block queue, and a second plurality of similarities areobtained by obtaining a similarity between the orientation blocksubsequent to the orientation block put into the candidate orientationblock set and each orientation block in the candidate orientation blockset.

At step 1033, If each of the second plurality of similarities is lessthan a second preset value, the orientation block subsequent to theorientation block put into the candidate orientation block set will beput into the candidate orientation block set.

At step 1034, If at least one of the second plurality of similarities islarger than or equal to the second preset value, the orientation blocksubsequent to the orientation block put into the candidate orientationblock set will be abandoned.

Then repeat step 1032 to step 1034 until the number of the candidateorientation blocks in the candidate orientation block set is up to apreset number.

Though the candidate orientation blocks selected from the orientationblock set according to the N similarities are very similar, all thecandidate orientation blocks may be wrong candidate orientation blocksfor the position (x_(i),y_(i)) when there is a strong local noise.Therefore, the candidate orientation blocks are selected according tonot only the similarities but also a diversification constraint toimprove a diversity of the candidate orientation blocks.

FIG. 8 is a schematic diagram of a comparison between the candidateorientation blocks selected according to the similarities and thecandidate orientation blocks selected according to both the similaritiesand the diversification constraint according to an embodiment of thepresent disclosure. As shown in FIG. 8, the candidate orientation blocksselected according to both the similarities and the diversificationconstraint are more similar to the image block of the fingerprint thanthe candidate orientation blocks selected according to the similarities.

At step 104, a plurality of compatibilities are obtained by obtaining acompatibility between two candidate orientation blocks corresponding toany two adjacent positions of the N positions respectively.

In one embodiment of the present disclosure, it is ensured that eachcandidate orientation block obtained according to the similarities issimilar to the initial orientation block, but it is not ensured that thecandidate orientation blocks of any two adjacent positions match witheach other. So it is necessary to obtain compatibilities between twocandidate orientation blocks corresponding to any two adjacent positionsof the N positions respectively. As two candidate orientation blockscorresponding to two adjacent positions have an overlap region, thecompatibility between the two candidate orientation blocks can bemeasured according to the similarity of the orientation field of theoverlap region.

FIG. 9 is a schematic diagram of the compatibility between two candidateorientation blocks corresponding to two adjacent positions according toan embodiment of the present disclosure, in which FIG. 9a is a schematicdiagram of two candidate orientation blocks with high compatibility, andFIG. 9b is a schematic diagram of two candidate orientation blocks withlow compatibility.

At step 105, a candidate orientation block for each position is obtainedfrom the preset number of candidate orientation blocks according to theP similarities and the plurality of compatibilities.

In one embodiment of the present disclosure, if k candidate orientationblocks have been obtained for each position, k×k compatibilities will beobtained as there is one compatibility between any two candidateorientation blocks corresponding to two adjacent positions respectively.

Specifically, an objective optimization function is establishedaccording to the P similarities and the plurality of compatibilities,and an optimized candidate orientation block for each position isobtained by optimizing the objective optimization function, in which theobjective optimization function is:

${{\min \mspace{14mu} {E(r)}} = {\min \left( {{\sum\limits_{i \in V}\left( {1 - {S\left( {\Theta_{i},\Phi_{i,r_{i}}} \right)}} \right)} + {w_{c}{\sum\limits_{{({i,j})} \in N}\left( {1 - {C\left( {\Phi_{i,r_{i}},\Phi_{j,r_{j}}} \right)}} \right)}}} \right)}},$

-   -   in which, V is the initial orientation field, i is a position        (x_(i),y_(i)) in the initial orientation field V, Θ_(i) is an        initial orientation block corresponding to the position        (x_(i),y_(i)), Θ_(i,r) _(i) is an r_(i) ^(th) candidate        orientation block corresponding to the position (x_(i),y_(i)),        S(Θ_(i),Θ_(i,r) _(i) ) is the similarity between the initial        orientation block Θi and the candidate orientation block Θ_(i,r)        _(i) , N is a set of four-connected adjacent initial orientation        blocks, Θ_(j,r) _(j) is an r_(j) ^(th) candidate orientation        block corresponding to a position (x_(j),y_(j)) adjacent to the        position (x_(i),y_(i)), C(Θ_(i,r) _(i) ,Θ_(j,r) _(j) ) is the        compatibility between the candidate orientation block Θ_(i,r)        _(i) and the candidate orientation block Θ_(j,r) _(j) , w_(c) is        a preset weight for weighting the similarity and the        compatibility.

In embodiments of the present disclosure, there are many existingmethods for optimizing the objective optimization function. Forinstance, the objective optimization function may be optimized accordingto methods (such as graph cut or confidence transmission) mentioned inA. Blake, P. Kohli, and C. Rother, Eds., Markov Random Fields for Visionand Image Processing, MIT Press, 2011. Finally one candidate orientationblock is selected from the k candidate orientation blocks as a finalorientation field for each position, and thus the orientation field ofthe fingerprint is obtained.

It should be noted that, in one embodiment of the present disclosure,the step of establishing the fingerprint dictionary is an off-line step,that is, only needs to be performed one time. Then the orientation fieldof the fingerprint can be estimated by consulting the fingerprintdictionary on-line.

With the method according to embodiments of the present disclosure, thecandidate orientation block for each position of the referencecoordinate system is obtained according to the similarities between eachof the N initial orientation blocks and each orientation block in the Norientation block sets, and the compatibilities between two candidateorientation blocks corresponding to any two adjacent positions of the Npositions, and thus a rational orientation field of the fingerprint isestimated by global optimization. In addition, with the method accordingto embodiments of the present disclosure, the orientation field isquantized, pre-knowledge regarding a fingerprint ridge is effectivelyapplied to the estimation of orientation field of the fingerprint, aninterference of a complicate background is greatly reduced, anefficiency of the fingerprint identification is increased, and anidentification precision for a poor quality fingerprint is significantlyimproved.

A device for estimating an orientation field of a fingerprint is furtherprovided according to embodiments of the present disclosure.

FIG. 10 is a block diagram of a device for estimating an orientationfield of a fingerprint according to an embodiment of the presentdisclosure. As shown in FIG. 10, the device for estimating theorientation field of the fingerprint comprises a first obtaining module10, a second obtaining module 20, a filtering module 30, a thirdobtaining module 40 and a generating module 50.

Specifically, the first obtaining module 10 is configured to receive afingerprint to be estimated, to obtain an initial orientation field ofthe fingerprint to be estimated, and to put the initial orientationfield in a reference coordinate system.

The second obtaining module 20 is configured to obtain N initialorientation blocks corresponding to the initial orientation field, inwhich the N initial orientation blocks correspond to N positions of theinitial orientation field in the reference coordinate systemrespectively, and to obtain N orientation block sets corresponding tothe N positions respectively from a fingerprint dictionary, in which thefingerprint dictionary comprises M orientation block sets correspondingto M positions in the reference coordinate system, and each of the Morientation block sets comprises a plurality of orientation blockscorresponding to a plurality of training fingerprints in one position ofthe M positions respectively and in which

The filtering module 30 is configured to obtain a similarity betweeneach of the N initial orientation blocks and each orientation block inthe N orientation block sets corresponding to the N positions to obtainP similarities, and to select a preset number of candidate orientationblocks for each of the N positions from the orientation block setaccording to the P similarities.

Specifically, the Q_(i) similarities are obtained as a similaritybetween the initial orientation block corresponding to the position(x_(i),y_(i)) and each orientation block in the orientation block setD(x_(i),y_(i)) is obtained. Then, a preset number of candidateorientation blocks for the position (x_(i),y_(i)) are selected from theorientation block set according to the Q_(i) similarities. In oneembodiment of the present disclosure, the preset number may be 6.

It should be noted that the number Q_(i) may be different from Q_(i) inwhich Q_(i) is a number of similarities between the initial orientationblock corresponding to the position (x_(j),y_(j)) and each orientationblock in the orientation block set D(x_(j),y_(j)), and iε(1,N), jε(1,N).

In one embodiment of the present disclosure, the filtering module 30 isspecifically configured to: f. obtain an orientation block queue bysorting orientation blocks of the orientation block set D(x_(i),y_(i))according to the similarities corresponding to the orientation blocks ofthe orientation block set D(x_(i),y_(i)), obtain a maximum similarity ofthe similarities, and put the orientation block corresponding to themaximum similarity into a candidate orientation block set; g. select anorientation block subsequent to the orientation block put into thecandidate orientation block set from the orientation block queue andobtain a similarity between the orientation block subsequent to theorientation block put into the candidate orientation block set and eachorientation block in the candidate orientation block set to obtain asecond plurality of similarities; h. if each of the second plurality ofsimilarities is less than a second preset value, put the orientationblock subsequent to the orientation block put into the candidateorientation block set into the candidate orientation block set; i. if atleast one of the second plurality of similarities is larger than orequal to the second preset value, abandon the orientation blocksubsequent to the orientation block put into the candidate orientationblock set; and j. repeat step g to step i until the number of thecandidate orientation blocks in the candidate orientation block set isup to a preset number.

Though the candidate orientation blocks selected from the orientationblock set according to the N similarities are very similar, all thecandidate orientation blocks may be wrong candidate orientation blocksfor the position (x_(i),y_(i)) when there is a strong local noise.Therefore, the candidate orientation blocks are selected according tonot only the similarities but also a diversification constraint toimprove a diversity of the candidate orientation blocks.

FIG. 8 is a schematic diagram of a comparison between the candidateorientation blocks selected according to the similarities and thecandidate orientation blocks selected according to both the similaritiesand the diversification constraint according to an embodiment of thepresent disclosure. As shown in FIG. 8, the candidate orientation blocksselected according to both the similarities and the diversificationconstraint are more similar to the image block of the fingerprint thanthe candidate orientation blocks selected according to the similarities.

The third obtaining module 40 is configured to obtain a compatibilitybetween two candidate orientation blocks corresponding to any twoadjacent positions of the N positions respectively to obtain a pluralityof compatibilities.

In one embodiment of the present disclosure, it is ensured that eachcandidate orientation block obtained according to the similarities issimilar to the initial orientation block, but it is not ensured that thecandidate orientation blocks of any two adjacent positions match witheach other. So it is necessary to obtain compatibilities between twocandidate orientation blocks corresponding to any two adjacent positionsof the N positions respectively. As two candidate orientation blockscorresponding to two adjacent positions have an overlap region, thecompatibility between the two candidate orientation blocks can bemeasured according to the similarity of the orientation field of theoverlap region.

FIG. 9 is a schematic diagram of the compatibility between two candidateorientation blocks corresponding to two adjacent positions according toan embodiment of the present disclosure, in which FIG. 9a is a schematicdiagram of two candidate orientation blocks with high compatibility, andFIG. 9b is a schematic diagram of two candidate orientation blocks withlow compatibility.

The generating module 50 is configured to obtain a candidate orientationblock for each position from the preset number of candidate orientationblocks according to the P similarities and the plurality ofcompatibilities.

In one embodiment of the present disclosure, if k candidate orientationblocks have been obtained for each position, k×k compatibilities will beobtained as there is one compatibility between any two candidateorientation blocks corresponding to two adjacent positions respectively.

Specifically, an objective optimization function is establishedaccording to the P similarities and the plurality of compatibilities,and an optimized candidate orientation block for each position isobtained by optimizing the objective optimization function, in which theobjective optimization function is:

${{\min \mspace{14mu} {E(r)}} = {\min \left( {{\sum\limits_{i \in V}\left( {1 - {S\left( {\Theta_{i},\Phi_{i,r_{i}}} \right)}} \right)} + {w_{c}{\sum\limits_{{({i,j})} \in N}\left( {1 - {C\left( {\Phi_{i,r_{i}},\Phi_{j,r_{j}}} \right)}} \right)}}} \right)}},$

-   -   in which, V is the initial orientation field, i is a position        (x_(i),y_(i)) in the initial orientation field V, Θi is an        initial orientation block corresponding to the position        (x_(i),y_(i)), Θ_(i,r) _(i) is an r_(i) ^(th) candidate        orientation block corresponding to the position (x_(i),y_(i)),        S(θi,Θ_(i,r) _(i) ) is the similarity between the initial        orientation block Θi and the candidate orientation block Θ_(i,r)        _(i) , N is a set of four-connected adjacent initial orientation        blocks, Θ_(j,r) _(j) is an r_(j) ^(th) candidate orientation        block corresponding to a position (x_(j),y_(j)) adjacent to the        position (x_(i),y_(i)), C(Θ_(i,r) _(i) ,Θ_(j,r) _(j) ) is the        compatibility between the candidate orientation block Θ_(i,r)        _(i) and the candidate orientation block Θ_(j,r) _(j) , w_(c) is        a preset weight for weighting the similarity and the        compatibility.

In embodiments of the present disclosure, there are many existingmethods for optimizing the objective optimization function. Forinstance, the objective optimization function may be optimized accordingto methods (such as graph cut or confidence transmission) mentioned inA. Blake, P. Kohli, and C. Rother, Eds., Markov Random Fields for Visionand Image Processing, MIT Press, 2011. Finally one candidate orientationblock is selected from the k candidate orientation blocks as a finalorientation field for each position, and thus the orientation field ofthe fingerprint is obtained.

It should be noted that, in one embodiment of the present disclosure,the step of establishing the fingerprint dictionary is an off-line step,that is, only needs to be performed one time. Then the orientation fieldof the fingerprint can be estimated by consulting the fingerprintdictionary on-line.

FIG. 11 is a block diagram of a device for estimating an orientationfield of a fingerprint according to an embodiment of the presentdisclosure. In one embodiment of the present disclosure, in order toensure the fingerprint dictionary is real and representative, thefingerprint dictionary may be established according to statisticalresults of orientation fields of a group of real training fingerprintscalibrated manually. As shown in FIG. 11, the device for estimating theorientation field of the fingerprint comprises a first obtaining module10, a second obtaining module 20, a filtering module 30, a thirdobtaining module 40, a generating module 50 and a dictionaryestablishing module 60. The first obtaining module 10 comprises a thirdobtaining unit 11, a dividing unit 12, a four obtaining unit 13, aorientation field establishing unit 14, and a generating unit 15, andthe dictionary establishing module 60 comprises a first obtaining unit61, a adjusting unit 62 and a second obtaining unit 63.

Specifically, the third obtaining unit 11 is configured to obtain aforeground image of the fingerprint to be estimated. In one embodimentof the present disclosure, if the fingerprint is collected from a scene(such as a crime scene), the foreground image should be manuallyextracted. If the fingerprint is from a fingerprint bank, since abackground thereof is very simple, the foreground image can be obtainedby applying Fourier transform to the image blocks of the fingerprintrespectively without manual extraction. Specifically, for each imageblock, two waves having the most and the secondary strongest frequencyresponse are obtained by Fourier transform, and then the foregroundimage can be obtained by comparing a ratio of two amplitudes of the twowaves with a threshold.

The dividing unit 12 is configured to divide the foreground image into aplurality of non-overlapping and second preset size image blocks. In oneembodiment of the present disclosure, the second preset size can be16×16 pixels.

The four obtaining unit 13 is configured to apply two-dimensionalshort-time Fourier transform to each of the plurality of non-overlappingand second preset size image blocks, and to obtain a plurality ofresponse directions corresponding to the plurality of non-overlappingand second preset size image blocks respectively. In one embodiment ofthe present disclosure, each image block can be regarded as atwo-dimensional surface wave and can be processed by the two-dimensionalshort-time Fourier transform. Then, a strongest response is found in afrequency domain, such that an orientation having the strongest responseis obtained by calculating an angle of the strongest response withrespect to a center of the frequency domain.

The orientation field establishing unit 14 is configured to establish aforeground orientation field of the fingerprint to be estimatedaccording to the plurality of response directions.

The generating unit 15 is configured to generate the initial orientationfield of the fingerprint to be estimated by adjusting the foregroundorientation field according to an adjusting algorithm for fingerprintattitude. In one embodiment of the present disclosure, the foregroundorientation field can be adjusted to the initial orientation field inthe reference coordinate system according to any of existing adjustingalgorithms for fingerprint attitude.

The dictionary establishing module 60 is configured to establish thefingerprint dictionary.

The first obtaining unit 61 is configured to obtain P trainingorientation fields corresponding to P training fingerprintsrespectively, and to calibrate P reference points corresponding to the Ptraining orientation fields respectively and P reference directionscorresponding to the P training orientation fields respectively. In oneembodiment of the present disclosure, firstly, P valid areas with highimage quality (i.e., high enough to tell orientation) are calibratedmanually in the P training fingerprints respectively. Subsequently, theP training orientation fields of the P training fingerprints arecalibrated manually in the P valid areas respectively. Then the Preference points corresponding to the P training orientation fieldsrespectively and the P reference directions corresponding to the Ptraining orientation fields are calibrated manually.

FIG. 4 is a schematic diagram of a reference point and a referencedirection of a training fingerprint according to an embodiment of thepresent disclosure. Specifically, as shown in FIG. 4, the referencepoint (point r shown in FIG. 4) is a midpoint between point a and pointb shown in FIG. 4, and the reference direction may be directed frompoint b to point a, in which point a is a vertex of a lowest ridgeextending from left to right of the training fingerprint and in an upperpart of the training fingerprint, and point b is a midpoint of a highestridge in a lower part of the training fingerprint. Alternatively, thereference direction may be directed from point b to point a.

The adjusting unit 62 is configured to adjust the P training orientationfields to the reference coordinate system according to the P referencepoints and the P reference directions respectively, and to obtain Preference orientation fields corresponding to the P trainingfingerprints respectively. In one embodiment of the present disclosure,the P training orientation fields can be adjusted according to thereference coordinate system. For instance, the P training orientationfields are processed by a rotation, a translation and a directioninterpolation respectively, such that the reference point of eachtraining orientation field is adjusted to an origin of the referencecoordinate system, and the reference direction of each trainingorientation field is adjusted to a positive direction of y axis of thereference coordinate system. The adjusted training orientation field isused as the reference orientation field of the training fingerprint.

FIG. 5a is a schematic diagram of the training orientation field beforeadjustment according to an embodiment of the present disclosure, andFIG. 5b is a schematic diagram of the training orientation field afteradjustment according to an embodiment of the present disclosure.

The second obtaining unit 63 is configured to obtaining the Morientation block sets according to the P reference orientation fieldsto establish the fingerprint dictionary. In one embodiment of thepresent disclosure, the P training orientation fields can be adjustedaccording to the reference coordinate system. For instance, as shown inFIG. 6, obtaining the M orientation block sets according to the Preference orientation fields comprises the following steps.

At step 601, a first preset size orientation block corresponding to aposition (x_(i),y_(i)) in each of the P reference orientation fields isobtained.

In one embodiment of the present disclosure, the first preset sizeorientation block is obtained by sliding a first preset size window oneach of the P reference orientation fields from left to right and fromtop to bottom. In one embodiment of the present disclosure, the firstpreset size is d×d(in which d may be of any value, such as d=4), whichmeans that the window contains d×d image blocks (in one embodiment, asize of each image block may be 16×16 pixels). Each image block containsone direction of the reference orientation field. As the window slidesat the position (x_(i),y_(i)), one first preset size orientation blockis obtained.

At step 602, it is judged whether the first preset size orientationblock is in a valid region, and if yes, execute step 603.

Specifically, if each image block in the first preset size orientationblock contains a direction of the reference orientation field, the firstpreset size orientation block is in the valid region, and if a imageblock in the first preset size orientation block does not contain thedirection of the reference orientation field, the first preset sizeorientation block is not in the valid region.

At step 603, the first preset size orientation block is put into a validset T(x_(i),y_(i)) corresponding to the position (x_(i),y_(i)).

For each reference orientation field, if the first preset sizeorientation block corresponding to the position (x_(i),y_(i)) is in thevalid region of the reference orientation field, the first preset sizeorientation block will be put into the valid set T(x_(i),y_(i))corresponding to the position (x_(i),y_(i)). Thus, one valid setcontaining a plurality of first preset size orientation blocks isobtained corresponding to the position (x_(i),y_(i)), and a plurality ofvalid sets are obtained corresponding to a plurality of positions in thereference coordinate system. FIG. 7 is a schematic diagram of valid setsT(−3,−3) and T(3,3) corresponding to position (−3,−3) and position (3,3) according to an embodiment of the present disclosure.

At step 604, an orientation block set D(x_(i),y_(i)) corresponding tothe position (x_(i),y_(i)) is obtained by clustering orientation blocksin the valid set T(x_(i),y_(i)).

As the number of the training fingerprints rising, the number oforientation blocks in each valid set rises, which will increase aworkload of fingerprint matching and reduce an efficiency of estimatingthe orientation field of the fingerprint. So each valid set should beclustered.

Firstly, an empty orientation block set D(x_(i),y_(i)) is initiallygiven, and any orientation block of the valid set T(x_(i),y_(i)) is putinto the orientation block set D(x_(i),y_(i)). Subsequently, anotherorientation block is selected from the valid set T(x_(i),y_(i)), and afirst plurality of similarities are obtained by obtaining a similaritybetween the another orientation block and each orientation block of theorientation block set D(x_(i),y_(i)). If each of the first plurality ofsimilarities is less than a first preset value, the another orientationblock will be put into the orientation block set D(x_(i),y_(i)). If atleast one of the first plurality of similarities is larger than or equalto the first preset value, the another orientation block will beabandoned. Clustering orientation blocks in the valid set T(x_(i),y_(i)) is finished until all the orientation blocks in the validset T(x_(i),y_(i)) are selected to be put into the orientation block setD(x_(i),y_(i)) or to be abandoned.

For instance, as shown in FIG. 7, orientation block sets D(−3,−3) andD(3, 3) corresponding to position (−3,−3) and position (3, 3)respectively are obtained by clustering the valid sets T(−3,−3) andT(3,3) respectively.

With the device according to embodiments of the present disclosure, thecandidate orientation block for each position of the referencecoordinate system is obtained according to the similarities between eachof the N initial orientation blocks and each orientation block in the Norientation block sets, and the compatibilities between two candidateorientation blocks corresponding to any two adjacent positions of the Npositions, and thus a rational orientation field of the fingerprint isestimated by global optimization. In addition, with the device accordingto embodiments of the present disclosure, the orientation field isquantized, pre-knowledge regarding a fingerprint ridge is effectivelyapplied to the estimation of orientation field of the fingerprint, aninterference of a complicate background is greatly reduced, anefficiency of the fingerprint identification is increased, and anidentification precision for a poor quality fingerprint is significantlyimproved.

Any procedure or method described in the flow charts or described in anyother way herein may be understood to comprise one or more modules,portions or parts for storing executable codes that realize particularlogic functions or procedures. Moreover, advantageous embodiments of thepresent disclosure comprises other implementations in which the order ofexecution is different from that which is depicted or discussed,including executing functions in a substantially simultaneous manner orin an opposite order according to the related functions. This should beunderstood by those skilled in the art which embodiments of the presentdisclosure belong to.

The logic and/or step described in other manners herein or shown in theflow chart, for example, a particular sequence table of executableinstructions for realizing the logical function, may be specificallyachieved in any computer readable medium to be used by the instructionexecution system, device or equipment (such as the system based oncomputers, the system comprising processors or other systems capable ofobtaining the instruction from the instruction execution system, deviceand equipment and executing the instruction), or to be used incombination with the instruction execution system, device and equipment.

It is understood that each part of the present disclosure may berealized by the hardware, software, firmware or their combination. Inthe above embodiments, a plurality of steps or methods may be realizedby the software or firmware stored in the memory and executed by theappropriate instruction execution system. For example, if it is realizedby the hardware, likewise in another embodiment, the steps or methodsmay be realized by one or a combination of the following techniquesknown in the art: a discrete logic circuit having a logic gate circuitfor realizing a logic function of a data signal, an application-specificintegrated circuit having an appropriate combination logic gate circuit,a programmable gate array (PGA), a field programmable gate array (FPGA),etc.

Those skilled in the art shall understand that all or parts of the stepsin the above exemplifying method of the present disclosure may beachieved by commanding the related hardware with programs. The programsmay be stored in a computer readable storage medium, and the programscomprise one or a combination of the steps in the method embodiments ofthe present disclosure when run on a computer.

Reference throughout this specification to “an embodiment,” “someembodiments,” “an example,” “a specific example,” or “some examples,”means that a particular feature, structure, material, or characteristicdescribed in connection with the embodiment or example is included in atleast one embodiment or example of the present disclosure. Theappearances of the phrases throughout this specification are notnecessarily referring to the same embodiment or example of the presentdisclosure. Furthermore, the particular features, structures, materials,or characteristics may be combined in any suitable manner in one or moreembodiments or examples.

Although explanatory embodiments have been shown and described, it wouldbe appreciated by those skilled in the art that the above embodimentscannot be construed to limit the present disclosure, and changes,alternatives, and modifications can be made in the embodiments withoutdeparting from spirit, principles and scope of the present disclosure.

What is claimed is:
 1. A method for estimating an orientation field of a fingerprint, comprising: receiving a fingerprint to be estimated, obtaining an initial orientation field of the fingerprint to be estimated, and putting the initial orientation field in a reference coordinate system; obtaining N initial orientation blocks corresponding to the initial orientation field, in which the N initial orientation blocks correspond to N positions of the initial orientation field in the reference coordinate system respectively, and obtaining N orientation block sets corresponding to the N positions respectively from a fingerprint dictionary, in which the fingerprint dictionary comprises M orientation block sets corresponding to M positions in the reference coordinate system, and each of the M orientation block sets comprises a plurality of orientation blocks corresponding to a plurality of training fingerprints in one position of the M positions respectively and in which N≦M; obtaining a similarity between each of the N initial orientation blocks and each orientation block in the N orientation block sets corresponding to the N positions to obtain P similarities, and selecting a preset number of candidate orientation blocks for each of the N positions from the orientation block set according to the P similarities; obtaining a compatibility between two candidate orientation blocks corresponding to any two adjacent positions of the N positions respectively to obtain a plurality of compatibilities; and obtaining a candidate orientation block for each position from the preset number of candidate orientation blocks according to the P similarities and the plurality of compatibilities.
 2. The method according to claim 1, wherein the fingerprint dictionary is established by: obtaining P training orientation fields corresponding to P training fingerprints respectively, and calibrating P reference points corresponding to the P training orientation fields respectively and P reference directions corresponding to the P training orientation fields respectively; adjusting the P training orientation fields to the reference coordinate system according to the P reference points and the P reference directions respectively, and obtaining P reference orientation fields corresponding to the P training fingerprints respectively; and obtaining the M orientation block sets according to the P reference orientation fields to establish the fingerprint dictionary.
 3. The method according to claim 2, wherein obtaining the M orientation block sets according to the P reference orientation fields comprises: obtaining a first preset size orientation block corresponding to a position (x_(i),y_(i)) in each of the P reference orientation fields; judging whether the first preset size orientation block is in a valid region; if yes, putting the first preset size orientation block into a valid set T(x_(i),y_(i)) corresponding to the position (x_(i),y_(i)); and obtaining an orientation block set D(x_(i),y_(i)) corresponding to the position (x_(i),y_(i)) by clustering orientation blocks in the valid set T(x y_(i)).
 4. The method according to claim 3, wherein clustering orientation blocks in the valid set T(x_(i),y_(i)) comprises: a. putting any orientation block of the valid set T(x_(i),y_(i)) into the orientation block set D(x_(i),y_(i)); b. selecting another orientation block from the valid set T(x_(i),y_(i)), and obtaining a similarity between the another orientation block and each orientation block of the orientation block set D(x_(i),y_(i)) to obtain a first plurality of similarities; c. if each of the first plurality of similarities is less than a first preset value, putting the another orientation block into the orientation block set D(x_(i),y_(i)); d. if at least one of the first plurality of similarities is larger than or equal to the first preset value, abandoning the another orientation block; and e. repeating step b to d until all the orientation blocks of the valid set T(x_(i),y_(i)) have been selected.
 5. The method according to claim 1, wherein obtaining an initial orientation field of the fingerprint to be estimated comprises: obtaining a foreground image of the fingerprint to be estimated; dividing the foreground image into a plurality of non-overlapping and second preset size image blocks; applying two-dimensional short-time Fourier transform to each of the plurality of non-overlapping and second preset size image blocks, and obtaining a plurality of response directions corresponding to the plurality of non-overlapping and second preset size image blocks respectively; establishing a foreground orientation field of the fingerprint to be estimated according to the plurality of response directions; and generating the initial orientation field of the fingerprint to be estimated by adjusting the foreground orientation field according to an adjusting algorithm for fingerprint attitude.
 6. The method according to claim 1, wherein selecting preset number of candidate orientation blocks for each of the N positions from the orientation block set corresponding to the initial orientation block according to the P similarities comprises: f. obtaining an orientation block queue by sorting orientation blocks of the orientation block set D(x_(i),y_(i)) according to the similarities corresponding to the orientation blocks of the orientation block set D(x_(i),y_(i)), obtaining a maximum similarity of the similarities, and putting the orientation block corresponding to the maximum similarity into a candidate orientation block set; g. selecting an orientation block subsequent to the orientation block put into the candidate orientation block set from the orientation block queue, and obtaining a similarity between the orientation block subsequent to the orientation block put into the candidate orientation block set and each orientation block in the candidate orientation block set to obtain a second plurality of similarities; h. if each of the second plurality of similarities is less than a second preset value, putting the orientation block subsequent to the orientation block put into the candidate orientation block set into the candidate orientation block set; i. if at least one of the second plurality of similarities is larger than or equal to the second preset value, abandoning the orientation block subsequent to the orientation block put into the candidate orientation block set; and j. repeating step g to i until the number of the candidate orientation blocks in the candidate orientation block set is a preset number.
 7. The method according to claim 1, wherein obtaining a candidate orientation block for each position from the preset number of candidate orientation blocks according to the P similarities and the plurality of compatibilities comprises: establishing an objective optimization function according to the P similarities and the plurality of compatibilities, and obtaining the candidate orientation block for each position by optimizing the objective optimization function, in which the objective optimization function is: ${{\min \mspace{14mu} {E(r)}} = {\min \left( {{\sum\limits_{i \in V}\left( {1 - {S\left( {\Theta_{i},\Phi_{i,r_{i}}} \right)}} \right)} + {w_{c}{\sum\limits_{{({i,j})} \in N}\left( {1 - {C\left( {\Phi_{i,r_{i}},\Phi_{j,r_{j}}} \right)}} \right)}}} \right)}},$ in which, V is the initial orientation field, i represents a position (x_(i),y_(i)) in the initial orientation field V, Θi is an initial orientation block corresponding to the position (x_(i),y_(i)), Θ_(i,r) _(i) is an r_(i) ^(th) candidate orientation block corresponding to the position (x_(i),y_(i)), S(Θ_(i),Θ_(i,r) _(i) ) is the similarity between the initial orientation block Θ_(i) and the candidate orientation block Θ_(i,r) _(i) , N is a set of four-connected adjacent initial orientation blocks, Θ_(j,r) _(j) is an r_(j) ^(th) candidate orientation block corresponding to a position (x_(j),y_(j)) adjacent to the position (x_(i),y_(i)), C(Θ_(i,r) _(i) ,Θ_(j,r) _(j) ) is the compatibility between the candidate orientation block Θ_(i,r) _(i) and the candidate orientation block Θ_(j,r) _(j) , w_(c) is a preset weight.
 8. The method according to claim 7, wherein the objective optimization function is optimized according to Graph Cut or confidence transmission method.
 9. A device for estimating an orientation field of a fingerprint, comprising: a first obtaining module, configured to receive a fingerprint to be estimated, to obtain an initial orientation field of the fingerprint to be estimated, and to put the initial orientation field in a reference coordinate system; a second obtaining module, configured to obtain N initial orientation blocks corresponding to the initial orientation field, in which the N initial orientation blocks correspond to N positions of the initial orientation field in the reference coordinate system respectively, and to obtain N orientation block sets corresponding to the N positions respectively from a fingerprint dictionary, in which the fingerprint dictionary comprises M orientation block sets corresponding to M positions in the reference coordinate system, and each of the M orientation block sets comprises a plurality of orientation blocks corresponding to a plurality of training fingerprints in one position of the M positions respectively and in which N≦M; a filtering module, configured to obtain a similarity between each of the N initial orientation blocks and each orientation block in the N orientation block sets corresponding to the N positions to obtain P similarities, and to select a preset number of candidate orientation blocks for each of the N positions from the orientation block set according to the P similarities; a third obtaining module, configured to obtain a compatibility between two candidate orientation blocks corresponding to any two adjacent positions of the N positions respectively to obtain a plurality of compatibilities; and a generating module, configured to obtain a candidate orientation block for each position from the preset number of candidate orientation blocks according to the P similarities and the plurality of compatibilities.
 10. The device according to claim 9, further comprising a dictionary establishing module configured to establish the fingerprint dictionary, wherein the dictionary establishing module comprises: a first obtaining unit, configured to obtain P training orientation fields corresponding to P training fingerprints respectively, and to calibrate P reference points corresponding to the P training orientation fields respectively and P reference directions corresponding to the P training orientation fields respectively; an adjusting unit, configured to adjust the P training orientation fields to the reference coordinate system according to the P reference points and the P reference directions respectively, and to obtain P reference orientation fields corresponding to the P training fingerprints respectively; and a second obtaining unit, configured to obtaining the M orientation block sets according to the P reference orientation fields to establish the fingerprint dictionary.
 11. The device according to claim 10, wherein the second obtaining unit is configured to: obtain a first preset size orientation block corresponding to a position (x_(i),y_(i)) in each of the P reference orientation fields; judge whether the first preset size orientation block is in a valid region; if yes, put the first preset size orientation block into a valid set T(x_(i),y_(i)) corresponding to the position (x_(i),y_(i)); and obtain an orientation block set D(x_(i),y_(i)) corresponding to the position (x_(i),y_(i)) by clustering orientation blocks in the valid set T(x_(i),y_(i)).
 12. The device according to claim 11, wherein clustering orientation blocks in the valid set T(x_(i),y_(i)) comprises: a. putting any orientation block of the valid set T(x_(i),y_(i)) into the orientation block set D(x_(i),y_(i)); b. selecting another orientation block from the valid set T(x_(i),y_(i)), and obtaining a similarity between the another orientation block and each orientation block of the orientation block set D(x_(i),y_(i)) to obtain a first plurality of similarities; c. if each of the first plurality of similarities is less than a first preset value, putting the another orientation block into the orientation block set D(x_(i),y_(i)); d. if at least one of the first plurality of similarities is larger than or equal to the first preset value, abandoning the another orientation block; and e. repeating step b to d until all the orientation blocks of the valid set T(x_(i),y_(i)) have been selected.
 13. The device according to claim 9, wherein the first obtaining module comprises: a third obtaining unit, configured to obtain a foreground image of the fingerprint to be estimated; a dividing unit, configured to divide the foreground image into a plurality of non-overlapping and second preset size image blocks; a four obtaining unit, configured to apply two-dimensional short-time Fourier transform to each of the plurality of non-overlapping and second preset size image blocks, and to obtain a plurality of response directions corresponding to the plurality of non-overlapping and second preset size image blocks respectively; a orientation field establishing unit, configured to establish a foreground orientation field of the fingerprint to be estimated according to the plurality of response directions; and a generating unit, configured to generate the initial orientation field of the fingerprint to be estimated by adjusting the foreground orientation field according to an adjusting algorithm for fingerprint attitude.
 14. The device according to claim 9, wherein the filtering module is configured to: f. obtain an orientation block queue by sorting orientation blocks of the orientation block set D(x_(i),y_(i)) according to the similarities corresponding to the orientation blocks of the orientation block set D(x_(i),y_(i)), obtain a maximum similarity of the similarities, and put the orientation block corresponding to the maximum similarity into a candidate orientation block set; g. select an orientation block subsequent to the orientation block put into the candidate orientation block set from the orientation block queue and obtain a similarity between the orientation block subsequent to the orientation block put into the candidate orientation block set and each orientation block in the candidate orientation block set to obtain a second plurality of similarities; h. if each of the second plurality of similarities is less than a second preset value, put the orientation block subsequent to the orientation block put into the candidate orientation block set into the candidate orientation block set; i. if at least one of the second plurality of similarities is larger than or equal to the second preset value, abandon the orientation block subsequent to the orientation block put into the candidate orientation block set; and j. repeat step g to i until the number of the candidate orientation blocks in the candidate orientation block set is a preset number.
 15. The device according to claim 9, wherein the generating module is configured to establish an objective optimization function according to the P similarities and the plurality of compatibilities, and to obtain the candidate orientation block for each position by optimizing the objective optimization function, in which the objective optimization function is: ${{\min \mspace{14mu} {E(r)}} = {\min \left( {{\sum\limits_{i \in V}\left( {1 - {S\left( {\Theta_{i},\Phi_{i,r_{i}}} \right)}} \right)} + {w_{c}{\sum\limits_{{({i,j})} \in N}\left( {1 - {C\left( {\Phi_{i,r_{i}},\Phi_{j,r_{j}}} \right)}} \right)}}} \right)}},$ in which, V is the initial orientation field, i is a position (x_(i),y_(i)) in the initial orientation field V, Θ_(i) is an initial orientation block corresponding to the position (x_(i),y_(i)), Θ_(i,r) _(i) is an r_(i) ^(th) candidate orientation block corresponding to the position (x_(i),y_(i)), S(θi,Θ_(i,r) _(i) ) is the similarity between the initial orientation block Θ_(i) and the candidate orientation block Θ_(i,r) _(i) , N is a set of four-connected adjacent initial orientation blocks, Θ_(j,r) _(j) is an r_(j) ^(th) candidate orientation block corresponding to a position (x_(j),y_(j)) adjacent to the position (x_(i),y_(i)), C(Θ_(i,r) _(i) , Θ_(j,r) _(j) ) is the compatibility between the candidate orientation block Θ_(i,r) _(i) and the candidate orientation block Θ_(j,r) _(j) , w_(c) is a preset weight.
 16. The device according to claim 15, wherein the objective optimization function is optimized according to Graph Cut or confidence transmission method. 